Multiframe blind deconvolution of atmospheric turbulence-degraded images based on filter

Jianming Huang; Mangzuo Shen; Qiang Li

doi:10.1117/12.755053

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29 November 2007 Multiframe blind deconvolution of atmospheric turbulence-degraded images based on filter

Jianming Huang, Mangzuo Shen, Qiang Li

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Proceedings Volume 6833, Electronic Imaging and Multimedia Technology V; 68331U (2007) https://doi.org/10.1117/12.755053
Event: Photonics Asia 2007, 2007, Beijing, China

Abstract

Blind deconvolution is a significant technology in the restoration of atmospheric turbulence-degraded images. However, if the atmospheric turbulence-degraded images are contaminated by noise, the restoration images will be beyond real image due to involving a mount of noise. A novel blind deconvolution method has been proposed. In this method, the degraded image is preprocessed by a linear filter for reducing noise, and the filter is considered in the cost function of blind deconvolution. An alternating minimization algorithm based on conjugate gradient method is applied for minimizing the cost function. Thus, the smoothness induced by linear filter and the blur induced by atmospheric turbulence are eliminated in blind deconvolution simultaneously. For verifying this method, the images degraded by turbulence with atmospheric seeing parameter equal to 0.1 meters for 2 meters telescope and contaminated by noise with signal noise ratio equal to 10 dB are simulated by computer and restored by this method. The experiment result demonstrates that the noise is reduced without introducing any smoothing and the degraded image are restored effectively. The image restored by this method is compared with by the blind deconvolution method based on edge preserving regularization. The result shows that the effect of reducing noise of our method is better than the latter.

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Jianming Huang, Mangzuo Shen, and Qiang Li "Multiframe blind deconvolution of atmospheric turbulence-degraded images based on filter", Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 68331U (29 November 2007); https://doi.org/10.1117/12.755053

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